For wireless communication systems a geographic area is divided into a plurality of cells, generally with at least one base station in each cell. A mobile communication unit wishing to transmit information, e.g. voice or data, establishes a communication link to one or more base stations. (Generally, but not invariably, the contacted base stations are located spatially near the mobile unit.) The information is transmitted from the mobile unit to the base station and then from the base station into the communication network. Although requirements vary depending on the technology and bandwidth, acceptable operation of the system for voice calls typically requires signal strength of approximately −95 dBm or greater.
Numerous system parameters affect the quality of system operation. For example, the number of base stations, the geographic location of these base stations, the type and number of antennae at each base station, the orientation of each antenna (azimuthal angle 1 FIG. 1, and radial angle 2 in FIG. 1 relative to projection 3), transmit power, pilot power, carrier frequency and antenna type all typically influence system efficacy. (Such parameters include both discrete parameters, e.g. antenna type, and continuous parameters, e.g. antenna orientation parameters.) Additionally, there are generally constraints on these parameters and on the system. (In the context of this description, both types of constraints are subsumed by the term constraints.) Constraints on parameters are imposed by physical and practical considerations. As exemplary of the former, the mounting brackets on antennae often physically preclude some portion of the azimuthal and radial angular spectrum. Similarly, topography such as hills and/or structures such as tall buildings make antenna azimuthal and radial angles directed toward such natural and man-made obstacles impractical. System constraints are exemplified by antenna radiation patterns, a variety of technology requirements some of which are imposed by a previously agreed upon standards, and the signal processing design of mobile terminals and base stations. Thus, in determining the potential quality of a communication system the parameters should be considered in view of these constraints.
Since a plethora of parameters and constraints influence the system, and since generally there is a strong interdependence between and among parameters, any attempt to find an optimal design and implement such a system involves an extremely complicated calculation dependent on these parameters and constraints. Indeed, the extent of interdependence in a wireless system environment makes the problem particularly difficult. Therefore the search for optimal designs has not been carried out directly, but instead has advanced through the search for superior if suboptimal designs based on a variety of tractable algorithms.
A measure of effectiveness of such an algorithm is the improvement in calculated system performance (from the inception of calculations to the conclusion) per unit time of calculation. One of the most commonly employed approximations for satisfying the improvement per unit time criterion involves an iterative approach to optimization. Two or more specialized algorithms each of which optimizes a subset of the parameters is employed iteratively to satisfy the improvement/unit time criteria. (Optimized parameters in the context of this disclosure is not necessarily the best choice of parameters possible, but a choice of parameters that improves the calculated performance of the system.) For example, it is often necessary for the design and construction of a system to perform a calculation to choose a number of base locations from an ensemble of possibilities, e.g., choose 70 locations for base station placement from 100 possibilities, and to set the azimuthal and radial orientation for each antenna at each chosen location. To make the problem manageable, in one approach, an iterative procedure is employed at each cycle of the iterations including, as applied to the earlier example, A) starting with all possible sites and optimizing the antennae orientations or choosing a starting orientation for the antennae B) optimizing by a suitable algorithm to eliminate locations e.g. 5 locations, and then C) optimizing by a suitable algorithm angles for the antennae (e.g. 3 antennae), at each remaining location. (The actual number of antennae at each location as previously discussed is a system constraint.) Thus, as an illustration a flow chart for the example (pick 70 locations from 100 possible locations and set the orientation for the three antennae at each of the 70 chosen locations) is shown in FIG. 2. In the first step 21, the antennae angles are initialized, for example, to be the allowed value closest to pointing out to the horizon with equal spacing in the azimuthal direction for the 300 possible antennae (3 at each of 100 possible locations). An antenna optimization algorithm is then utilized to choose desirable orientations for each of the antennae. Then in step 22 a location optimization is used to eliminate a number of locations, e.g. 5. (Of course fewer locations could be eliminated in such step but at a cost of substantially increased computing time or more locations could be eliminated at the cost of decreased performance improvement. The number of locations actually eliminated at each step is a matter of choice depending on the system design and construction goals.) The antennae angle optimization algorithm in step 23 is then used to choose a desirable orientation for each of the remaining 285 antennae distributed at the remaining 95 locations. Fixing the antennae orientation determined in step 23 the procedure then cycles with the location optimization algorithm in step 24, choosing 90 of the remaining 95 locations. (It is necessary to perform the antennae optimization for desirable improvement after each elimination step due to the strong interdependence of location and antennae orientation parameters as discussed earlier.) In the continuing iterative procedure the orientation of the remaining 270 antennae are optimized in step 25. Alternation continues until 70 locations with a total of 210 optimized antennae orientations are chosen. In all, 6 location optimizations are performed on respectively 100, 95, 90, 85, 80, and 75 locations as well as 7 antennae optimization procedures on respectively 300, 285, 270, 255, 240, 225 and 210 antennae.
Thus despite the simplification of fixing parameters or optimizing over a subset of variables, the problem still requires multiple optimization steps, each of which is a difficult optimization of a large number of parameters, e.g. antennae orientation parameters and antennae locations. Accordingly, although simplification is achieved, the approach is nevertheless not simple. Substantial time (generally 1 week or more) even on a high speed computer is still needed to obtain an accurate solution for even modest system design problems. Decreasing the required time for obtaining an accurate solution provides many advantages. For example, it is possible to perform the solution process repeatedly in an acceptable time period to accommodate the evolution of design goals and constraints inherent in reaching a design that satisfies the various evolving exigencies envisioned for the system. Additionally, the ability to reach a rapid solution aids in the adjustments and engineering choices involved in constructing a system. As a result both the design and construction of wireless communication systems would benefit from an approach that enhances the performance improvement per unit calculation time for determinations that are part of the design and/or construction process.